IP Library › Granted Patent US 12,308,122
Granted Patent B2
US 12,308,122 · App. 18/596,720 · Granted May 20, 2025

Applying predictive models to data representing a history of events

Inventors: Constantinos Ioannis Boussios (Chelsea, MA); Francis Thomas O'Donovan (Arlington, MA); Richard Gliklich (Weston, MA)
Assignee: OM1, Inc.
G16H50/20G06F16/2462G06F16/2477G06F18/2148G06F18/217G06N20/00G16H50/70
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Quick Facts
Patent No.
US 12,308,122
App. No.
18/596,720
Granted
May 20, 2025
Kind
B2
Abstract

A predictive model can be applied to data representing a history of events for an entity to compute a value indicative of an outcome related to a reference time for that entity. The effect of an event from an entity's history of events on an outcome for the entity at a reference time can vary based on the type of event and relative time of that event with respect to the reference time. The effect of an event from an entity's history of events on an outcome for the entity also can vary due to other characteristics of the entity in combination with the event. These effects are captured as weights. For an entity, functions of sets of events from the history of events are computed for the entity and a set of weights for events. The computed results are inputs to the predictive model.

Claims (38)

1. A computer system, comprising:

a processing system comprising a processing device and computer storage medium;

a predictive model comprising computer program code stored in the computer storage medium and processed by the processing system and having an input that receives data values for input features derived from event data from historical data for an entity and an output that provides data representing a result from the predictive model processing the received data values for the input features, wherein the result comprises a value indicative of a predicted outcome for the entity relative to an input reference time;

a timeline generation module comprising computer program code stored in the computer storage medium and processed by the processing system and having an input to receive event data from historical data for an entity, and an output that provides a plurality of timelines for the entity, each timeline comprising data representing a respective set of events from the received event data;

a set of weights stored in the computer storage wherein the set of weights comprises a respective distinct weight for each tuple in a plurality of tuples, each tuple representing a respective distinct combination of at least a type of event, a respective relative time of the event with respect to a reference time, and one or more entity profile characteristics for an entity;

a calculation module comprising computer program code stored in the computer storage medium and processed by the processing system and having a first input that receives the plurality of timelines provided by the timeline generation module for the entity and a second input that receives the input reference time, the calculation module:

for each timeline, accessing, from the set of weights in the computer storage, a respective weight for each event in the timeline, based on the type of the event, the respective relative time of the event with respect to the input reference time, and one or more entity profile characteristics of the entity, and

for each timeline, computing a respective additional feature for the entity as a respective function of the retrieved respective weights for each event in the timeline; and

wherein the predictive model receives, as the input features, at least the data values derived from the event data for the set of events from the historical data for the entity and the respective additional features computed for the timelines for the entity computed by the calculation module, and wherein the predictive model computes the predicted outcome for the entity relative to the input reference time based on both the data values for the features derived from the event data from the historical data for the entity and the additional features computed for the timelines for the entity.

2. The computer system of claim 1 , wherein the input reference time comprises a current time.

3. The computer system of claim 1 , wherein the input reference time comprises a time associated with an event.

4. The computer system of claim 1 , wherein the input reference time comprises a time for which the outcome of the predicted model is computed.

5. The computer system of claim 1 , wherein the respective function for a timeline among the plurality of timelines is different from the respective function for at least one other timeline among the plurality of timelines.

6. The computer system of claim 1 , wherein the function comprises a linear function.

7. The computer system of claim 1 , wherein the function comprises a non-linear function.

8. The computer system of claim 1 , wherein each unique tuple in the set of weights has a single weight.

9. The computer system of claim 1 , wherein at least one tuple in the set of weights comprises a plurality of weights, and the calculation module selects from among the plurality of weights.

10. The computer system of claim 1 , wherein the historical data for entities comprises patient medical histories, and wherein the entity comprises a patient, and the entity profile characteristic comprises at least one of age, a comorbidity, a behavior, a characteristic from a family history, or genetic profile attribute of the patient, and wherein categories of events include at least one of a medical diagnosis, a medical procedure, a medical treatment, a medical laboratory result, or a medication prescribed or purchased or administered for a patient, and wherein types of events correspond to one or more event codes or one or more medical instances.

11. The computer system of claim 1 , wherein the set of weights comprises a plurality of weight tables, including a first weight table for a first outcome and a second weight table for a second outcome different from the first outcome, wherein a first predictive model generates values indicative of the first outcome using the first weight table, and a second predictive model generates values indicative of the second outcome using the second weight table.

12. The computer system of claim 1 , wherein the set of weights comprises a weight table corresponding to a first outcome, and wherein the predictive model outputs a value indicative of a second outcome different from the first outcome.

13. The computer system of claim 12 , wherein the second outcome is correlated with the first outcome.

14. The computer system of claim 1 , wherein the set of weights comprises a plurality of weight tables, wherein the calculation module accesses the plurality of weight tables to compute the results provided as inputs to the predictive model.

15. The computer system of claim 1 , wherein the predictive model generates a value indicative of a first outcome for an entity, wherein the first outcome is correlated to a second outcome, and the computer system reports a value indicative of the second outcome for the entity based on the value indicative of the first outcome for the entity.

16. The computer system of claim 10 , wherein a plurality of types of events are grouped together as a medical instance, and wherein at least one weight in the set of weights is associated with the medical instance.

17. The computer system of claim 10 , wherein the plurality of categories of events include at least two of procedures codes, medication codes, or diagnosis codes.

18. The computer system of claim 1 , wherein the set of weights comprises a plurality of different weights for a type of event for different combinations of that type of event with different relative times.

19. The computer system of claim 1 , wherein for a first tuple having a first weight for a first combination of a first type of event and a first relative time, and a second tuple having a second weight for a second combination of the first type of event and a second relative time longer than the first relative time, the first weight is less than the second weight.

20. The computer system of claim 19 , wherein for a third tuple having a third weight for a third combination of a second type of event and a third relative time, and a fourth tuple having a fourth weight for a fourth combination of the second type of event and a fourth relative time longer than the third relative time, the third weight is greater than the fourth weight.

21. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value indicative of a probability the entity has the outcome.

22. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value from a set of discrete values.

23. The computer system of claim 22 , wherein the discrete range of values comprises a finite set of integers comprising at least 1 to 1000.

24. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value from a range of continuous values.

25. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value from a scale that ranks or categorizes entities with respect to the outcome.

26. The computer system of claim 25 , wherein the scale comprises a finite set of integers comprising at least 1 to 1000.

27. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value indicative of a probability the entity has the outcome.

28. The computer system of claim 1 , wherein the result output for an entity by the predictive model comprises a value indicative of an estimation of risk that the entity has the outcome.

29. The computer system of claim 1 , further comprising a timeline generation module converting event data for an entity into a timeline, and wherein the calculation module computes a function of the weights from the set of weights and event data in the timeline to generate an input for the predictive model.

30. The computer system of claim 1 , wherein the relative time for events in the set of weights is computed in units of months.

Assignments (1)
SECURITY INTEREST Recorded Aug 12, 2025
From: OM1, INC.
To: COMERICA BANK
Reel/Frame 071997/0292 →
Continuity (3)
Continuation 16386123 · Apr 16, 2019
Provisional Application 62658868 · Apr 17, 2018
Related Publication 20240290491A1 · Aug 29, 2024
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